1. Field of the Invention
The present invention is directed to a computerized method for classifying a time series containing a prescribable number of samples, such as an electrical signal.
2. Description of the Prior Art
In many technical fields it is of interest to draw conclusions about the future behavior of the time series from measured time series. The prediction of the future "behavior" of the time series ensues given the assumption that the time series comprises nonlinear correlations or linear correlations (statistical dependencies) between the samples of the time series.
This problem also obtains to considerable significance in various medical fields, for example in cardiology. Specifically in the problem area of sudden cardiac death, it can be vital to recognize early warning signs of sudden cardiac death in order to initiate counter-measures against the occurrence of sudden cardiac death as early as possible.
It is known that a time series of an electrocardiogram that is not correlated describes a heart that is not at risk with respect to sudden cardiac death. A heart at risk with respect to sudden cardiac death is described by a time series of the electrocardiogram that comprises non-linear correlations between the samples of the time series G. Morfill, "Komplexitatsanalyse in der Kardiologie," Physikalische Blatter, Vol. 50, No. 2, pp. 156-160, (1994). It is also known from the Morfill article to determine time series of an electrocardiogram that describe hearts that are at risk with respect to sudden cardiac death from the graphic phase space presentation of two successive heartbeats.
LICOX, GMS, Gesellschaft fur Medizinische Sondentecknik mbH, Advanced Tissue Monitoring discloses a method with which the time curve of the local oxygen voltage of the brain (tip02) can be determined.
The method disclosed in the Morfill article exhibits all of the disadvantages that are typical of empirical methods. In particular, the error susceptibility of graphic interpretations by a human, the problem of setting a threshold from which a time series is classified as at risk, as well as imprecisions in the presentation of the Fourier transform on the picture screen are considered disadvantageous in the known method.